The present invention relates generally to acoustic source separation and localization and more particularly to acoustic source separation with a microphone array wherein a moving microphone array is simulated.
Acoustic localization and analysis of multiple industrial sound sources such as motors, pumps etc., are challenging as their frequency content is largely time invariant and emissions of similar machines are highly correlated. Therefore, standard assumptions for localization, taken e.g. in DUET as described in “[I] J S. Rickard, R. Balan, and J. Rosca. Real-Time Time-Frequency Based Blind Source Separation. In Proc. of International Conference on Independent Component Analysis and Signal Separation (ICA2001), pages 651-656, 2001” such as disjoint time-frequency content of the sources, do not hold, and yield unsatisfactory results.
More powerful Bayesian DOA methods such as MUST as described in “[2] T. Wiese, H. Claussen, J. Rosca. Particle Filter Based DOA for Multiple Source Tracking (MUST). To be published in Proc. of ASILOMAR, 2011” assume knowledge of the number of sources. It is, however, difficult to estimate this for correlated sources in echoic environments. Source localization is very difficult if sources are possibly in the near field of the microphones. It is challenging to test and account for the presence of these sources.
One possible approach is to increase the number of synchronously sampled microphones in an array. However, this results in extremely high data-rates and is too computationally expensive
Accordingly, improved and novel methods and systems for computationally tractable source separation and localization are required.